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All Journal Jurnal Teknologi Industri Pertanian Seminar Nasional Informatika (SEMNASIF) JOIN (Jurnal Online Informatika) Abdimas Pedagogi: Jurnal Ilmiah Pengabdian kepada Masyarakat JOIV : International Journal on Informatics Visualization Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Ecodemica : Jurnal Ekonomi Manajemen dan Bisnis Jurnal Teknik Informatika STMIK Antar Bangsa JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Ekonomi, Manajemen Akuntansi dan Perpajakan (Jemap) J I M P - Jurnal Informatika Merdeka Pasuruan Applied Information System and Management Jurnal Teknoinfo Jurnal Nasional Komputasi dan Teknologi Informasi Energi & Kelistrikan Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika CSRID (Computer Science Research and Its Development Journal) Jurnal Ilmu Komputer dan Bisnis Aisyah Journal of Informatics and Electrical Engineering Jurnal Sistem informasi dan informatika (SIMIKA) Journal of Innovation and Future Technology (IFTECH) TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Intelligent Computing and Health Informatics (JICHI) Jurnal Sistem Informasi Jurnal Sains Indonesia Bulletin of Computer Science Research Journal of Students‘ Research in Computer Science (JSRCS) Journal Software, Hardware and Information Technology Jurnal Mandiri IT J-Intech (Journal of Information and Technology) Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Sains dan Teknologi Jurnal Sains Informatika Terapan (JSIT) Paradigma Indonesian Journal Computer Science (ijcs) Jurnal Ilmiah Teknik Informatika dan Komunikasi Innovative: Journal Of Social Science Research Jurnal Komputer dan Teknologi (JUKOMTEK) Bulletin of Artificial Intelligence Riau Jurnal Teknik Informatika Journal of Information Technology
Sumanto, Sumanto
Universitas Bina Sarana Informatika
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Komparasi Algoritma Machine Learning (SVM, Random Forest, dan Regresi Logistik) untuk Prediksi Tingkat Obesitas Achmad Rivai Syahputra; Rian Hidayat; Fathur Rismansyah; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 5 No. 3 (2025): November: Jurnal Ilmiah Teknik Informatika dan Komunikasi 
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v5i3.1716

Abstract

Obesity is a global health issue with a continuously increasing prevalence. Early prediction of obesity levels is crucial for designing more effective intervention strategies. This study aims to apply and analyze the performance of three machine learning classification methods: Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR), for predicting obesity levels. The research methodology utilizes a public dataset, ObesityLevels, downloaded from the Kaggle platform, which consists of 2111 medical and lifestyle records. The process includes data preprocessing to convert categorical features into numerical ones, splitting the data into training and testing sets with a 70:30 ratio, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that the Random Forest (RF) algorithm achieved the highest performance, with an accuracy of 90.3%, precision of 90.3%, recall of 90.3%, and an F1-score of 90.3%. Based on these findings, it is concluded that the Random Forest model is the most effective choice for an obesity level prediction system based on the dataset used.
Combination of Response to Criteria Weighting Method and Multi-Attribute Utility Theory in the Decision Support System for the Best Supplier Selection Faruk Ulum; Junhai Wang; Dyah Ayu Megawaty; Ari Sulistiyawati; Riska Aryanti; Sumanto Sumanto; Setiawansyah Setiawansyah
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1810

Abstract

Choosing the right supplier is a strategic factor in supporting operational efficiency and a company's competitive advantage. This process requires a decision support system that is able to assess various alternatives objectively and in a structured manner. This study aims to develop a decision support system in the selection of the best supplier by combining the Response to Criteria Weighting (RECA) and Multi-Attribute Utility Theory (MAUT) methods. The RECA method is used to objectively determine the weight of each criterion based on the variation of data between alternatives, so as to reduce subjectivity in the weighting process. Meanwhile, the MAUT method functions to calculate the total utility value of each supplier based on the normalization value and weight that has been obtained. The results of the RECA method show the objective weight of each criterion, which is then used in the MAUT calculation process. The results of the analysis, obtained in the best supplier selection based on the total score of each candidate, it can be seen that PT Global Niaga Mandiri ranks first with the highest score of 0.6512, this shows that this company is the best choice in the supplier selection process. In second place is UD Anugrah Bersama with a score of 0.399, followed by PT Indo Logistik Prima in third place with a score of 0.3451. The combination of the RECA and MAUT methods has been proven to be able to produce accurate, rational, and accountable decisions. This system provides a measurable approach in filtering supplier alternatives efficiently and is relevant to be applied to various other multi-criteria decision-making contexts.
Perbandingan Hasil Klasifikasi Decision Tree dan Naïve Bayes dalam Memprediksi Churn Nasabah Bank Rizal Maulana; Fardha Hasykir; Muhammad Furqon Prasetyo; Rafi Kurniawan; Sumanto Sumanto; Andi Diah Kuswanto
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 3 (2025): Juni 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i3.9218

Abstract

Abstrak - Nasabah bank adalah individu  yang memiliki hubungan keuangan dengan bank, seperti simpanan, pinjaman, atau layanan lainnya. Fenomena Churn menjadi perhatian penting karena dapat mempengaruhi pendapatan dan stabilitas lembaga perbankan. Penelitian ini bertujuan untuk memprediksi churn nasabah menggunakan algoritma machine learning Decision Tree dan Naive Bayes. Model ini dianalisis untuk menentukan tingkat AUC (Area Under The Curve), CA (Classification Accuracy), dan F1 Score, serta menilai efektivitasnya dalam kategori klasifikasi.Hasil penelitian menunjukkan bahwa algoritma Decision Tree mampu mencapai tingkat akurasi yang cukup baik, dengan nilai accuracy (CA) sebesar 85.4% sedangkan Naïve Bayes memiliki nilai accuracy sebesar 84.5%.  Nilai akurasi ini menunjukkan bahwa Decision Tree berada dalam kategori Good Classification dan dapat digunakan sebagai alat yang handal dalam mengidentifikasi nasabah yang berisiko churn. Temuan ini mendukung potensi penerapan machine learning dalam strategi retensi pelanggan di sektor perbankan. Studi ini juga membuka peluang untuk pengembangan lebih lanjut, termasuk integrasi dengan metode klasifikasi lain atau pemanfaatan teknik seleksi fitur untuk meningkatkan akurasi prediksi churn.Kata kunci: Naive Bayes; Decision Tree; Klasifikasi; Churn; Nasabah Bank Abstract - Bank customers are individuals who have financial relationships with banks, such as deposits, loans, or other services. Churn phenomenon is an important concern because it can affect the income and stability of banking institutions. This research aims to predict customer churn using Decision Tree and Naive Bayes machine learning algorithms. The model is analyzed to determine the level of AUC (Area Under The Curve), CA (Classification Accuracy), and F1 Score, as well as assess its effectiveness in the classification category. The results show that the Decision Tree algorithm is able to achieve a fairly good level of accuracy, with an accuracy value (CA) of 85.4% while Naïve Bayes has an accuracy value of 84.5%. These accuracy values indicate that Decision Tree is in the Good Classification category and can be used as a reliable tool in identifying customers at risk of churn. These findings support the potential application of machine learning in customer retention strategies in the banking sector. This study also opens up opportunities for further development, including integration with other classification methods or utilization of feature selection techniques to improve churn prediction accuracy.Keywords: Naïve Bayes; Decision Tree; Classification; Churn; Bank Customers
Prediksi Pertumbuhan Penduduk Kota Jakarta Timur Menggunakan Metode Regresi Linear Anita Adelia Syahfitri; Sumanto Sumanto
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 4 (2025): Agustus 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i4.9431

Abstract

Abstrak - Pertumbuhan penduduk merupakan fenomena alami yang ditandai dengan peningkatan atau penurunan jumlah populasi di suatu wilayah. Sebagai wilayah dengan distribusi penduduk tertinggi di Provinsi DKI Jakarta, Kota Jakarta Timur menghadapi tantangan dalam penyediaan infrastruktur publik serta pengelolaan sumber daya akibat tingginya tingkat pertumbuhan penduduk. Penelitian ini bertujuan untuk melakukan prediksi terhadap jumlah penduduk Kota Jakarta Timur di masa mendatang menggunakan metode regresi linear sederhana. Data yang digunakan berupa data deret waktu (time series) jumlah penduduk pada periode 2010 hingga 2024. Hasil penelitian menunjukkan bahwa model regresi linear memiliki tingkat akurasi sangat baik dengan R-Squared sebesar 0,956. Nilai ini menunjukkan 95,6% variasi jumlah penduduk dapat dijelaskan oleh variabel tahun. Selain itu, hasil uji hipotesis menunjukkan variabel tahun memiliki pengaruh signifikan terhadap pertumbuhan jumlah penduduk di Kota Jakarta Timur.Kata Kunci: Pertumbuhan Penduduk; Prediksi; Regresi Linear; Jakarta Timur;  Abstract - Population change is a natural occurrence reflected in the rising or declining number of inhabitants within a specific area. As the area with the highest population concentration in DKI Jakarta Province, East Jakarta experiences notable challenges in delivering adequate public facilities and managing regional resources, primarily driven by its rapid demographic expansion. This research is conducted to forecast the future population of East Jakarta using a simple linear regression technique. The dataset consists of time series records on the number of residents spanning from 2010 to 2024. The findings reveal that the applied regression model achieves a high prediction accuracy, with an R-Squared value of 0,956. This indicates that 95,6% of the variability in population figures can be attributed to the year variable. Moreover, hypothesis testing confirms that the time variable significantly influences that increase in population size in East Jakarta.Keywords: Population Growth; Prediction; Linear Regression; East Jakarta;
Co-Authors Achmad Rivai Syahputra Ade Budiman, Ade Ade Christian Ade Christian Ade Christian, Ade Adi Pangestu Adi Supriyatna Aditia Yudhistira Agung Wibowo Agus Buono Ahmad Habibullah Ahmad Yani ahmad yani Ahmad Yani , Ahmad Yani Andi Diah Kuswanto Andri Amico Anggreani, Namira Anita Adelia Syahfitri Apip Supiandi Ari Sulistiyawati Ari Sulistiyawati Arshad, Muhammad Waqas Arya, Yudi Aulia Rachmat, Daffa Azkia, Farah Diba Bib Paruhum Silalahi Bismo Raharjo, Yohanes Aryo Budhi Adhiani Christina Budi Santoso Budiman, Ade Surya Cahya, Titus Dwi Christian , Ade Damayanti Damayanti Dedi Darwis Dedi Triyanto DENY KURNIAWAN Dewi, Revinta Arrova Diah, Andi Dyah Ayu Megawaty Dyani Kalyana Mitta Eka Dyah Setyaningsih Eka Putri Alvi Syahrina Elisabeth Sri Hendrastuti Fahrian Faiz Djarot, Raihan Jamal Fajar Akbar Fajrian, Ihsan Fardha Hasykir Faruk Ulum Fathur Rismansyah Ganda Wijaya Ganda Wijaya, Ganda Hafis Nurdin Harianto Harianto Hariyanto HARIYANTO HARIYANTO Hartanti Hartanti Hidayat, Manarul Hilmy Ibrahim, Farras Imam Budiawan Indah Purwandani Indra Chaidir, Indra Indra, Ahmad Indriani , Karlena Indriyanti, Zahra Kiky Dwi Insani Abdi Bangsa Jumadi, Yakobus Linus Jumaryadi, Yuwan Junhai Wang Kadir, Fauwas Abdul Karlena Indriani Karlisa Priandana Kotjek, Rafie Kuswanto, Andi Diah Laura Gabriel da Silva Lia Mazia, Lia Lita Sari Marita Maharani Rona Makom Mantriwira, Daniel Mardinawat Mardinawat Marundrury, Aberahamo Onoma Megawaty, Dyah Ayu Mochamad Wahyudi Muhammad Furqon Prasetyo Nabilla, Adinda Naufal Hermawan, Rezan Nirwana Hendrastuty Noviyanto Nur Rachmat Nugraha Nurfia Oktaviani Syamsiah Oprasto, Raditya Rimbawan Paduloh Paduloh Pasaribu, A. Ferico Octaviansyah Permata, Permata Prasetyo, Romadhan Edy Pribadi, Denny Pricillia Pujiastuti, Lise Rafi Kurniawan Ramadani, Achmes Dade Ramadhan, Muhammad Gilang Ramadhani, Varla Octavia Rani, Maulidina Cahaya Rasendriya, Rafi Ratiyah* Ratiyah Reynaldi , Reynaldi Rian Hidayat Rifda Ilahy Rosihan Riska Aryanti Riska Aryanti Rizal Maulana Rizqi Ramadhani, Muhammad Roida Pakpahan Ruhul Amin Ruhul Amin Ruhul Amin, Ruhul Ruli , Ahmad Rais Rumidjan Rumidjan, Rumidjan Rusda Wajhillah Ryan Randy Suryono Sanriomi Sintaro Setiawan, Dandi Setiawansyah Setiawansyah Sri Hendrastuti, Elisabeth Sri Sugiharti SUKAMTI . Sumarna Sumarna Sumarna Sumarna Teguh Budhi Santosa Tri Widian Ratnasari Ulum, Faruk Umam, Hairul Ummu Radiyah, Ummu Vera Agustina Yanti Wahyudi, Agung Deni Wang, Junhai Wardani, Maidy Tri Wattilah, Florentina Wijaya, Filzah Yanuar Laik, Abraham Adrian Yundari, Yundari Yuri Rahmanto Zidan, Muhammad `Diah Kuswanto, Andi